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Predicting Rainfall Using Inclusive Multiple Model and Radial Basis Function Neural Network

In: Application of Machine Learning Models in Agricultural and Meteorological Sciences

Author

Listed:
  • Mohammad Ehteram

    (Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering)

  • Akram Seifi

    (Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture)

  • Fatemeh Barzegari Banadkooki

    (Payame Noor University, Agricultural Department)

Abstract

This study used the salp swarm algorithm (SSA), Henry gas solubility optimization algorithm (HGSOA), and crow optimization algorithm (COA) to adjust the radial basis function neural network (RABFN) parameters for predicting monthly rainfall. Then, a new ensemble model was created using the outputs of RABFN, RABFN-SSA, RABFN-HGSOA, and RABFN-COA. The new ensemble model was named inclusive multiple model (IMM). This study indicated that the ensemble models improved the efficiency of the optimized RABFN models. The training MAE of the IMM, RABFN-HGSOA, RABFN-SSA, RABFN-PSO, and RBFN models was 0.987, 1.35, 1.47, 1.58, and 2.21 mm. The IMM reduced the testing MAE of the IMM, RABFN-HGSOA, RABFN-SSA, RABFN-PSO, and RBFN models by 32%, 37%, 42%, and 55%, respectively. Also, the HGSOA had better performance than the SSA and COA.

Suggested Citation

  • Mohammad Ehteram & Akram Seifi & Fatemeh Barzegari Banadkooki, 2023. "Predicting Rainfall Using Inclusive Multiple Model and Radial Basis Function Neural Network," Springer Books, in: Application of Machine Learning Models in Agricultural and Meteorological Sciences, chapter 0, pages 101-115, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-9733-4_12
    DOI: 10.1007/978-981-19-9733-4_12
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